Efficient Drone Detection Method Based on YOLOv8s Improvement

Abstract

Combating illegal drone activities is an important task for national defense and security. How to spot drones quickly and accurately is the key. While there are many ways to detect drones, their reasoning is generally slow and complex. Therefore, in this work, we propose an improved and efficient UAV detection method YOLOv8s-C3AS based on YOLOv8s. There are three main improvements to this approach: First, we propose a new Coordinate Channel Spatial Attention Module (CCSM) and add it to the backbone of the model to enable better feature extraction. Secondly, in order to solve the scale inconsistency problem of YOLOv8s PANet, we propose a new adaptive fusion feature network (PANet-AF), which enables the model to fuse the features of the three scales better, which enables the model to better integrate features of different scales. Third, we use a more reasonable bounding box regression loss function SIoU, which improves the detection accuracy of the model without cost. Finally, we refined and made public the drone dataset and conducted a series of experiments combined with the PASCCOL VOC dataset. Our proposed approach achieves 77.2% mAP, 98.9% mAP50, 87.1% mAP75 and 120.5 FPS on the drone dataset. Experiments demonstrate that our proposed method outperforms other methods by achieving high detection accuracies while maintaining faster inference speed and lower model parameters. The drone-datasets used for this research has been uploaded to kaggle: https://www.kaggle.com/datasets/zhangtutu123/drone-dataset123/dat.